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A New Operator Extracting Image Patch Based on EPLL

  • Zhang, Jianwei (College of Math and Statistics, Nanjing University of Information Science and Technology) ;
  • Jiang, Tao (College of Math and Statistics, Nanjing University of Information Science and Technology) ;
  • Zheng, Yuhui (Jiangsu Engineering Centre of Network Monitoring, College of Computer and Software, Nanjing University of Information Science and Technology) ;
  • Wang, Jin (School of Computer & Communication Engineering, Changsha University of Science & Technology) ;
  • Xie, Jiacen (College of Math and Statistics, Nanjing University of Information Science and Technology)
  • Received : 2018.01.11
  • Accepted : 2018.05.01
  • Published : 2018.06.30

Abstract

Multivariate finite mixture model is becoming more and more popular in image processing. Performing image denoising from image patches to the whole image has been widely studied and applied. However, there remains a problem that the structure information is always ignored when transforming the patch into the vector form. In this paper, we study the operator which extracts patches from image and then transforms them to the vector form. Then, we find that some pixels which should be continuous in the image patches are discontinuous in the vector. Due to the poor anti-noise and the loss of structure information, we propose a new operator which may keep more information when extracting image patches. We compare the new operator with the old one by performing image denoising in Expected Patch Log Likelihood (EPLL) method, and we obtain better results in both visual effect and the value of PSNR.

Keywords

References

  1. A. Beck and M. Teboulle, "Fast gradient-based algorithms for constrained total variation image denoising and deblurring problems," IEEE Transactions on Image Processing, vol. 18, no. 11, pp. 2419-2434, 2009. https://doi.org/10.1109/TIP.2009.2028250
  2. W. Zuo, D. Meng, L. Zhang, X. Feng, and D. Zhang, "A generalized iterated shrinkage algorithm for non-convex sparse coding," in Proceedings of IEEE International Conference on Computer Vision, Sydney, Australia, 2013, pp. 217-224.
  3. M. Jung, X. Bresson, T. Chan, and L. Vese, "Nonlocal Mumford-Shah regularizers for color image restoration," IEEE Transactions on Image Processing, vol. 20, no. 6, pp. 1583-1598, 2011. https://doi.org/10.1109/TIP.2010.2092433
  4. Y. Zheng, K. Ma, Q. Yu, J. Zhang, and J. Wang, "Regularization parameter selection for total variation model based on local spectral response," Journal of Information Processing Systems, vol. 13, no. 5, pp. 1168-1182, 2017. https://doi.org/10.3745/JIPS.02.0072
  5. Y. Zheng, B. Jeon, J. Zhang, and Y. Chen, "Adaptively determining regularization parameters in nonlocal total variation regularization for image denoising," Electronics Letters, vol. 5, no. 2, pp. 144-145, 2015.
  6. Z. Yang and M. Jacob, "Nonlocal regularization of inverse problems: a unified variational framework," IEEE Transactions on Image Processing, vol. 22, no. 8, pp. 3192-3203, 2013. https://doi.org/10.1109/TIP.2012.2216278
  7. R. Yan, L. Shao, and Y. Liu, "Nonlocal hierarchical dictionary learning using wavelets for image denoising," IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4689-4698, 2013. https://doi.org/10.1109/TIP.2013.2277813
  8. L. Sun, B. Jeon, Y. Zheng, and Z. Wu, "Hyperspectral image restoration using low-rank representation on spectral difference image," IEEE Geoscience and Remote Sensing Letters, vol. 14, no. 7, pp. 1151-1155, 2017. https://doi.org/10.1109/LGRS.2017.2701805
  9. J. Han, R. Quan, D. Zhang, and F. Nie, "Robust object co-segmentation using background prior," IEEE Transactions on Image Processing, vol. 27, no. 4, pp. 1639-1651, 2018. https://doi.org/10.1109/TIP.2017.2781424
  10. F. Xiao, W. Liu, Z. Li, L. Chen, and R. Wang, " Noise-tolerant wireless sensor networks localization via multinorms regularized matrix Completion," IEEE Transactions on Vehicular Technology, vol. 67, no. 3, pp. 2409-2419, 2018. https://doi.org/10.1109/TVT.2017.2771805
  11. J. Hughes, M. Rockmore, and Y. Wang, "Bayesian learning of sparse multiscale image representation," IEEE Transactions on Image Processing, vol. 22, no. 12, pp. 4972-4983, 2013. https://doi.org/10.1109/TIP.2013.2280188
  12. S. Roth and M. J. Black. "Fields of experts," International Journal of Computer Vision, vol. 82, no. 2, article no. 205, 2009.
  13. S. Roth and M. J. Black, "Fields of experts: a framework for learning image priors," in Proceedings of IEEE Computer Society Conference on Computer Vision and Pattern Recognition, San Diego, CA, 2005, pp. 860-867, 2005.
  14. D. Zoran and Y. Weiss, "From learning models of natural image patches to whole image restoration," in Proceedings of IEEE International Conference on Computer Vision, Barcelona, Spain, 2011, pp. 479-486.
  15. D. Geman and C. Yang, "Nonlinear image recovery with half-quadratic regularization," IEEE Transactions on Image Processing, vol. 4, no. 7, pp. 932-946, 1995. https://doi.org/10.1109/83.392335
  16. G. Yu, G. Sapiro, and S. Mallat, "Solving inverse problems with piecewise linear estimators: from Gaussian mixture models to structured sparsity," IEEE Transactions on Image Processing, vol. 21, no. 5, pp. 2481-2499, 2012. https://doi.org/10.1109/TIP.2011.2176743
  17. C. Aguerrebere, A. Almansa, Y. Gousseau, and J. Delon, "Single shot high dynamic range imaging using piecewise linear estimators," in Proceedings of IEEE International Conference on Computational Photography, Santa Clara, CA, 2014, pp. 1-10.
  18. Y. Q. Wang and J. M. Morel, "SURE guided Gaussian mixture image denoising," SIAM Journal of Imaging Sciences, vol. 6, no. 2, pp. 999-1034, 2013. https://doi.org/10.1137/120901131
  19. R. Zhang, C. A. Bouman, J. B. Thibault, and K. D. Sauer, "Gaussian mixture Markov random field for image denoising and reconstruction," in Proceedings of IEEE Global Conference on Signal and Information Processing, Austin, TX, 2014, pp. 1089-1092.
  20. V. Papyan and M. Elad, "Multi-scale patch-based image restoration," IEEE Transactions on Image Processing, vol. 25, no. 1, pp. 249-261, 2016. https://doi.org/10.1109/TIP.2015.2499698
  21. Y. Zheng, X. Zhou, B. Jeon, J. Shen, and H. Zhang, "Multi-scale patch prior learning for image denoising using Student's-t mixture model," Journal of Internet Technology, vol. 18, no. 7, pp. 1553-1560, 2017.
  22. Y. Zheng, B. Jeon, L. Sun, J. Zhang, and H. Zhang, "Student's t-hidden Markov model for unsupervised learning using localized feature selection," IEEE Transactions on Circuits and Systems for Video Technology, 2017. https://doi.org/10.1109/TCSVT.2017.2724940.
  23. B. Ophir, M. Lustig, and M. Elad, "Multi-scale dictionary learning using wavelets," IEEE Journal of Selected Topics in Signal Processing, vol. 5, no. 5, pp. 1014-1024, 2011. https://doi.org/10.1109/JSTSP.2011.2155032
  24. X. Lu, Y. Yuan, and P. Yan, "Alternatively constrained dictionary learning for image superresolution," IEEE Transactions on Cybernetics, vol. 44, no. 3, pp. 366-377, 2014. https://doi.org/10.1109/TCYB.2013.2256347
  25. F. Xiao, Z. Wang, N. Ye, R. Wang, and X. Li, "One more tag enables fine-grained RFID localization and tracking," IEEE/ACM Transactions on Networking, vol. 26, no. 1, pp. 161-174, 2018. https://doi.org/10.1109/TNET.2017.2766526
  26. J. Han, D. Zhang, G. Cheng, N. Liu, and D. Xu, "Advanced deep-learning techniques for salient and category-specific object detection: a survey," IEEE Signal Processing Magazine, vol. 35, no. 1, pp. 84-100, 2018. https://doi.org/10.1109/MSP.2017.2749125
  27. M. Golipour, H. Ghassemian, and F. Mirzapour, "Integrating hierarchical segmentation maps with MRF prior for classification of hyperspectral images in a Bayesian framework," IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 2, pp. 805-816, 2016. https://doi.org/10.1109/TGRS.2015.2466657